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    <abstract>We present DOPA METER, a tool suite for the metrical investigation of written language, that provides diagnostic means for its division into discourse categories, such as registers, genres, and style. The quantitative basis of our system are 120 metrics covering a wide range of lexical, syntactic, and semantic features relevant for language profiling. The scores can be summarized, compared, and aggregated using visualization tools that can be tailored according to the users’ needs. We also showcase an application scenario for DOPA METER.</abstract>
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%0 Conference Proceedings
%T DOPA METER – A Tool Suite for Metrical Document Profiling and Aggregation
%A Lohr, Christina
%A Hahn, Udo
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F lohr-hahn-2023-dopa
%X We present DOPA METER, a tool suite for the metrical investigation of written language, that provides diagnostic means for its division into discourse categories, such as registers, genres, and style. The quantitative basis of our system are 120 metrics covering a wide range of lexical, syntactic, and semantic features relevant for language profiling. The scores can be summarized, compared, and aggregated using visualization tools that can be tailored according to the users’ needs. We also showcase an application scenario for DOPA METER.
%R 10.18653/v1/2023.emnlp-demo.18
%U https://aclanthology.org/2023.emnlp-demo.18
%U https://doi.org/10.18653/v1/2023.emnlp-demo.18
%P 218-228

Markdown (Informal)

[DOPA METER – A Tool Suite for Metrical Document Profiling and Aggregation](https://aclanthology.org/2023.emnlp-demo.18) (Lohr & Hahn, EMNLP 2023)

• DOPA METER – A Tool Suite for Metrical Document Profiling and Aggregation (Lohr & Hahn, EMNLP 2023)

ACL

• Christina Lohr and Udo Hahn. 2023. DOPA METER – A Tool Suite for Metrical Document Profiling and Aggregation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 218–228, Singapore. Association for Computational Linguistics.